Such stories are usually unconfirmed, exaggerated claims written by self-proclaimed “growth-hackers”. Bleh. We don’t learn a lot from them.

Sean’s story is different because he explains how and why Chin took certain steps. He shows us how to use data science to optimize and succeed in any ad campaign – even if you hate math.

Here are the lessons you can take from the story.

Have a clear goal

Data scientists like to joke “left unchecked, all optimization converges on gambling and porn”. Unfortunately too many digital advertisers run campaigns without having a clear criteria for success.

Here’s how you avoid this trap: Pretend you’ve been running your ad campaign for 3 months – what does success look like? More traffic? Higher sales? More followers? More likes? X number of comments by followers?

Chin wanted to build a new popular Facebook fan page using only curated content and a tiny ad budget.

“Popular” is a fine goal for a side project – but you’ll want more quantifiable goals.

Commit to making decisions based on data

I don’t know the best ad copy. I don’t know the best image.
I don’t know the best demographic … location…

You get the idea.

Be comfortable not knowing. Embrace the uncertainty and let data and evidence guide you.

It’s more comfortable (and faster) to assume you have the answers and just start running campaigns. Unless you’re lucky, this approach is almost always sub-optimal.

Chin started with a list of Facebook page topics like horror, gardening and weddings. He then ran a few ad campaigns, began measuring interest and converged on Clowns, Ghost, and Creepy Stuff.

Imagine what would have happened if Chin’s client (or a HiPPO) just picked a different topic like gardening? Chin let the data drive him to an answer.

Start with a few critical metrics then refine

Remember the first time you looked at Google Analytics? The data overwhelmed you.

Don’t get pulled into data paralysis. Ad networks and keyword tools usually give us way, way more than we need – especially when we are starting. You are better off starting with 1 or 2 simple, measurable metrics and refining them later. For instance, measure clicks on Twitter ad campaigns.

Chin started with Likes – the easiest metric – to filter out the worst topics. He then began measuring user engagement to identify the best topic: horror.

Force creativity. Be cheap

Budgeting for PPC is tough, especially from a data scientist’s perspective.

More data is always better. But the only way to get more data is to spend more on online advertising. This is a huge dilemma for advertisers: on one hand, you want to validate the ROI before sinking significant money in. On the other hand, you need data to confidently validate your investment.

Regardless of your budget, it’s important to remember to keep it lean and get creative to get the data you need for cheap. For AdWords, reconsider using some of these default settings that can hurt ROI from day 1.

Chin segmented his Facebook campaign by topics, spending $5 per day on each. The small spend forced him to focus on the most critical metrics and get creative with campaign structure.

Optimize by systematically testing variations

Want to waste a lot of time? Generate a bunch of ads “to see what works”. Brainstorming is great for generating initial ideas but you need a process to optimize anything or get real insight.

Choose 4 combinations running at the same time: 2 with the same image and 2 with the same copy. Then whichever ad combination performs best, use that to A/B test a different audience. Keep repeating this process with different variables until you find a combination that gets you the most fans at the cheapest price (e.g., $2 per click).

Being data-driven is a mindset

Data science isn’t about a hack. Or a tool. Or an equation. So don’t worry if you hate equations and don’t know the difference between linear regression and unhealthy obsession.